Study in a novel lithium-ion semi-solid-state batteries: Specific equivalent circuit modeling and improved SOC estimation method

Abstract

Semi-solid-state batteries (SSSBs) combine the high safety and energy density of solid-state batteries with the superior conductivity and longevity of liquid-state batteries. This paper presents a novel SSSB and introduces a targeted modeling and state of charge (SOC) estimation method for its optimization. Initially, by analyzing the internal structure and interfacial properties, the SSSB’s advantages over liquid-state and all-solid-state batteries are highlighted. Subsequently, electrochemical impedance spectroscopy and offline parameter identification techniques are employed to construct a specific equivalent circuit model (ECM) that incorporates the unique interfacial properties of the SSSB. This step ensures that the model accurately reflects the battery’s behavior under various conditions. Building on this foundation, an improved SOC correction method for end-of-discharge SOC correction is proposed. Based on the SOC estimation results from the extended Kalman filter and the backpropagation neural network (BPNN), a second-layer BPNN is further employed for model training. It trains on discharge data prior to the deviation of EKF’s end-of-discharge SOC estimation and corrects the terminal SOC, ensuring cost-effectiveness and timeliness. Finally, experimental validation demonstrates the effectiveness of this method in enhancing the accuracy and robustness of end-of-discharge SOC estimation for the SSSB. This work contributes to the development and application of lithium-ion SSSBs.